Across the last decades, various space missions have measured thetotal solar irradiance (TSI) such as the Variability of Irradiance andGravity Oscillations (VIRGO) experiment on the Solar and HeliosphericObservatory (SOHO) starting in 1996. Since the beginning of itsrecording time, one challenge is to correct the measurements from thedegradation of the TSI sensors in space. Various groupshave proposed different methodologies to produce a continuous TSI timeseries (TSI composite) which is essential to monitor the sun activityand its influence on the Earth’s climate. However, the benchmark to test all those solutions is source of adebate in the community. Moreover, the input data for the TSI compositeare the degradation-corrected measurements provided by each individualinstrument team. Here, we propose a different approach using amachine learning and data fusion algorithm to produce automatically thedegradation-corrected TSI time series based on a small number of genericassumptions. The algorithm is applied to the VIRGO/PMO6, VIRGO/DIARADand PREMOS/PMO6 data. The time series agree between each other in termsof mean value with a difference of ~ 0.14 W/m2 (PREMOS), ~ 0.23 W/m2(VIRGO) and ~ -0.18 W/m2 (DIARAD). Finally, taking a conservative valueof 0.3 W/m2 between our different TSI products, induces a variation ofthe global mean surface temperature of ~ 0.02 K based on global climatesimulations, which is within the uncertainties of simulated global meansurface temperatures, hence not impacting significantly any climateforcing scenarios.